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To illustrate the use of mixture theory to simultaneously optimize the macrocation and the macroanion composition of a hydroponical nutrient solution, a {3,2 3,2} double lattice design was established in the double mixture factor space (K+, Ca2+, Mg2+, NO−3, H2PO−4and SO2−4).
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This work proposes a simple, generic and efficient approach to combine process and mixture factors in the same design by handling easily any mixture constraint in the frame of fractional factorial designs.
The sum of all the ingredients is a constant total T, which is equal to 100% or 1 unless any constant mixture factors are present (Lundstedt et al. 1998).
The method used in this work is based on the use of mixture factors derived from the fractions of equivalents of the major ions present in solution combined with the total concentration as a process variable.
To address this problem, we develop a novel joint learning model for classification, referred to as two-layer mixture of factor analyzers with joint factor loading (2L-MJFA).
The deposition pressure, gas mixture dilution factor, deposition time, and the reaction gas flow ratios were found to be the most significant factors that influenced coating thickness.
The gas mixture compressibility factor along the reactor is shown in Fig. 3.
The manifold-CS model described above is known in statistics as a mixture of factor analyzers (MFA).
Hence a specific mixture assessment factor (MAF) has been discussed in order to safeguard against unwanted mixture effects from multi-component mixtures of partly unidentified composition[5].
Finally, we briefly discuss how to construct a robust version of the closely related mixture of factor analyzers.
GMM are used to represent the non-Gaussian sources, in which case it is an example of a model known as a mixture of factor analyzers [10].
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